{
  "nbformat": 4,
  "nbformat_minor": 2,
  "metadata": {
    "colab": {
      "name": "MLP.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/github/Taehee-K/Brain-Tumor-Classification/blob/main/code/MLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ],
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "source": [
        "import os\r\n",
        "path = os.path.dirname(os.path.abspath(__file__))\r\n",
        "os.chdir(path)"
      ],
      "outputs": [],
      "metadata": {
        "id": "mbF-t5mNPJP6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Split Data"
      ],
      "metadata": {
        "id": "0mlQ8IxbPV6x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Train, Validation, Test 데이터 폴더 나누기"
      ],
      "metadata": {
        "id": "nfurc7XoQROn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import shutil\r\n",
        " \r\n",
        "original_dataset_dir = './BrainTumorData'   \r\n",
        "classes_list = os.listdir(original_dataset_dir) \r\n",
        " \r\n",
        "base_dir = './splitted'                           # train-validation 데이터 나누어 저장\r\n",
        "os.mkdir(base_dir)\r\n",
        " \r\n",
        "train_dir = os.path.join(base_dir, 'train')       # train data\r\n",
        "os.mkdir(train_dir)\r\n",
        "test_dir = os.path.join(base_dir, 'val')          # test data\r\n",
        "os.mkdir(test_dir)\r\n",
        "\r\n",
        "for cls in classes_list:     \r\n",
        "    os.mkdir(os.path.join(train_dir, cls))\r\n",
        "    os.mkdir(os.path.join(test_dir, cls))"
      ],
      "outputs": [],
      "metadata": {
        "id": "vdgUY4_vPVS_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Train:Test 8:2 로 데이터 분할\n",
        "* 각 클래스 별 데이터 수 확인"
      ],
      "metadata": {
        "id": "ncEjsRz_QVoR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "import math\r\n",
        " \r\n",
        "for cls in classes_list:\r\n",
        "    path = os.path.join(original_dataset_dir, cls)\r\n",
        "    fnames = os.listdir(path)\r\n",
        " \r\n",
        "    train_size = math.floor(len(fnames) * 0.8)\r\n",
        "    test_size = math.floor(len(fnames) * 0.2)\r\n",
        "    \r\n",
        "    train_fnames = fnames[:train_size]\r\n",
        "    print(\"Train size(\",cls,\"): \", len(train_fnames))\r\n",
        "    for fname in train_fnames:\r\n",
        "        src = os.path.join(path, fname)\r\n",
        "        dst = os.path.join(os.path.join(train_dir, cls), fname)\r\n",
        "        shutil.copyfile(src, dst)\r\n",
        "        \r\n",
        "    test_fnames = fnames[train_size:]\r\n",
        "    print(\"Test size(\",cls,\"): \", len(test_fnames))\r\n",
        "    for fname in test_fnames:\r\n",
        "        src = os.path.join(path, fname)\r\n",
        "        dst = os.path.join(os.path.join(test_dir, cls), fname)\r\n",
        "        shutil.copyfile(src, dst)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train size( Brain Tumor ):  2010\n",
            "Test size( Brain Tumor ):  503\n",
            "Train size( Healthy ):  1669\n",
            "Test size( Healthy ):  418\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8aPlJ4qIQKyS",
        "outputId": "2088d200-e874-4730-99c6-ac87cec7ea7b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Modules"
      ],
      "metadata": {
        "id": "gY3Ec2iGQoQh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Load Data\r\n",
        "import torchvision.transforms as transforms\r\n",
        "from torchvision.datasets import ImageFolder \r\n",
        "from torch.utils.data import DataLoader\r\n",
        "\r\n",
        "# Pytorch --> MLP, CNN\r\n",
        "import torch\r\n",
        "import torch.nn as nn \r\n",
        "import torch.nn.functional as F\r\n",
        "import torch.optim as optim\r\n",
        "\r\n",
        "from torchsummary import summary"
      ],
      "outputs": [],
      "metadata": {
        "id": "4xWouD7KQnuo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "source": [
        "if torch.cuda.is_available():\r\n",
        "  DEVICE = torch.device('cuda')\r\n",
        "else:\r\n",
        "  DEVICE = torch.device('cpu')\r\n",
        "print(DEVICE)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "21eJSOYPQw5f",
        "outputId": "83267c0d-5f30-4d26-c2bb-59f39db66c03"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "source": [
        "BATCH_SIZE = 64\r\n",
        "EPOCH = 30"
      ],
      "outputs": [],
      "metadata": {
        "id": "zIDQtchdQxGe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "source": [
        "original_dataset_dir = './BrainTumorData'   \r\n",
        "classes_list = os.listdir(original_dataset_dir) \r\n",
        "\r\n",
        "n_classes = len(classes_list)\r\n",
        "print(classes_list)           # 분류해야 할 클래스들\r\n",
        "print(n_classes)      # 클래스 수 2개"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Brain Tumor', 'Healthy']\n",
            "2\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkMRfTeJQ2j-",
        "outputId": "a415a96f-720d-4a4b-cceb-eea8675e1f36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Data"
      ],
      "metadata": {
        "id": "QAqMDgOiUvSK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "transform_base = transforms.Compose([transforms.Resize((227,227)),\r\n",
        "                                     transforms.ToTensor(),\r\n",
        "                                     transforms.Grayscale(num_output_channels=1)]) \r\n",
        "train_dataset = ImageFolder(root='./splitted/train', transform=transform_base)\r\n",
        "test_dataset = ImageFolder(root='./splitted/val', transform=transform_base)"
      ],
      "outputs": [],
      "metadata": {
        "id": "hwA82Zz6RKq3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "source": [
        "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4)\r\n",
        "test_loader = torch.utils.data.DataLoader(test_dataset,batch_size=BATCH_SIZE, shuffle=True, num_workers=4)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvRneP0vRNsL",
        "outputId": "0ab0f5c2-8b4d-4e10-a561-912652a84af6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 데이터 개수, 형태 확인"
      ],
      "metadata": {
        "id": "Lo4OE5Wdb3xI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "source": [
        "for (X_train, y_train) in train_loader:\r\n",
        "    print('X_train:', X_train.size(),'type:', X_train.type())\r\n",
        "    print('y_train:', y_train.size(),'type:', y_train.type())\r\n",
        "    break"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train: torch.Size([64, 1, 227, 227]) type: torch.FloatTensor\n",
            "y_train: torch.Size([64]) type: torch.LongTensor\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqysFnG7bxZs",
        "outputId": "a22481e6-44c6-4af9-de31-e5cd9043510c"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 데이터 사진 확인하기"
      ],
      "metadata": {
        "id": "Hb9ryNtsTf8W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "source": [
        "pltsize = 2\r\n",
        "plt.figure(figsize=(10 * pltsize, pltsize))\r\n",
        "for i in range(10):\r\n",
        "    plt.subplot(1, 10, i + 1)\r\n",
        "    plt.axis('off')\r\n",
        "    plt.imshow(X_train[i, :, :, :].numpy().reshape(227, 227), cmap = \"gray_r\")\r\n",
        "    plt.title('Class: ' + str(y_train[i].item()))"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x144 with 10 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "id": "3PYcSlVGTdRx",
        "outputId": "499b9526-e7d2-43df-b859-2a979d7192a3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi-Layer Perceptron\n",
        "\n"
      ],
      "metadata": {
        "id": "qWITu3WVRI6n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* MLP\n"
      ],
      "metadata": {
        "id": "H4I1L6cOVLGH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![mlp](https://koalaverse.github.io/machine-learning-in-R/images/mlp_network.png)"
      ],
      "metadata": {
        "id": "RBSODlK0SEiG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "source": [
        "class MLP(nn.Module): # MLP 모델 설계\r\n",
        "    def __init__(self, n_classes = 2):   \r\n",
        "        super(MLP, self).__init__()\r\n",
        "        self.linear1 = nn.Linear(227*227, 512)\r\n",
        "        self.linear2 = nn.Linear(512, 256)\r\n",
        "        self.linear3 = nn.Linear(256, 2)\r\n",
        "\r\n",
        "    def forward(self, x):\r\n",
        "        x = x.view(-1, 227*227*1)\r\n",
        "        x = F.relu(self.linear1(x))\r\n",
        "        x = F.relu(self.linear2(x))\r\n",
        "        x = self.linear3(x)\r\n",
        "        x = F.log_softmax(x, dim=1)\r\n",
        "        \r\n",
        "        return x\r\n",
        "\r\n",
        "# print model summary\r\n",
        "mlp = MLP().to(DEVICE)\r\n",
        "summary(mlp, (1, 227, 227)) # summary code "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Linear-1                  [-1, 512]      26,383,360\n",
            "            Linear-2                  [-1, 256]         131,328\n",
            "            Linear-3                    [-1, 2]             514\n",
            "================================================================\n",
            "Total params: 26,515,202\n",
            "Trainable params: 26,515,202\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.20\n",
            "Forward/backward pass size (MB): 0.01\n",
            "Params size (MB): 101.15\n",
            "Estimated Total Size (MB): 101.35\n",
            "----------------------------------------------------------------\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M9yo9J_ORsdu",
        "outputId": "1b1429c4-3beb-45c1-cb48-01e646cc5c7b"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "source": [
        "optimizer = optim.Adam(mlp.parameters(), lr=0.001)\r\n",
        "criterion = nn.CrossEntropyLoss()"
      ],
      "outputs": [],
      "metadata": {
        "id": "-Cv7O0o0Rsw1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Train Model"
      ],
      "metadata": {
        "id": "rooJ91V3U86Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "source": [
        "def train(model, train_loader, optimizer):\r\n",
        "    model.train()                         # 모델 train 상태로\r\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\r\n",
        "        data, target = data.to(DEVICE), target.to(DEVICE)  # data, target 값 DEVICE에 할당\r\n",
        "        optimizer.zero_grad()                              # optimizer gradient 값 초기화\r\n",
        "        output = model(data)                               # 할당된 데이터로 output 계산\r\n",
        "        loss =  criterion(output, target)                  # Cross Entropy Loss 사용해 loss 계산\r\n",
        "        loss.backward()                                    # 계산된 loss back propagation\r\n",
        "        optimizer.step()                                   # parameter update"
      ],
      "outputs": [],
      "metadata": {
        "id": "NBMOFujjU9iL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Evaluate Model"
      ],
      "metadata": {
        "id": "9RxzeapWVC8-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "source": [
        "def evaluate(model, test_loader):\r\n",
        "    model.eval()      # 모델 평가 상태로\r\n",
        "    test_loss = 0     # test_loss 초기화\r\n",
        "    correct = 0       # 맞게 예측한 0 값으로 초기화\r\n",
        "    \r\n",
        "    with torch.no_grad(): \r\n",
        "        for data, target in test_loader:\r\n",
        "            data, target = data.to(DEVICE), target.to(DEVICE)     # data, target DEVICE에 할당\r\n",
        "            output = model(data)                                  # output 계산\r\n",
        "            test_loss += criterion(output, target).item()         # loss 계산(총 loss 에 더해주기)\r\n",
        "            pred = output.max(1, keepdim=True)[1]                 # 계산된 벡터값 중 가장 큰 값 가지는 class 예측\r\n",
        "            correct += pred.eq(target.view_as(pred)).sum().item() # 맞게 예측한 값 세기\r\n",
        "   \r\n",
        "    test_loss /= len(test_loader.dataset)                         # 평균 loss\r\n",
        "    test_accuracy = 100. * correct / len(test_loader.dataset)     # test(validation) 데이터 정확도\r\n",
        "    return test_loss, test_accuracy "
      ],
      "outputs": [],
      "metadata": {
        "id": "QzZpfMSrVDpF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train MLP"
      ],
      "metadata": {
        "id": "LfbgiyabVnIW"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "source": [
        "import time\r\n",
        "import copy\r\n",
        " \r\n",
        "def train_model(model ,train_loader, val_loader, optimizer, num_epochs = 30):\r\n",
        "    best_acc = 0.0  # beset accuracy 초기화\r\n",
        "    best_model_wts = copy.deepcopy(model.state_dict()) \r\n",
        " \r\n",
        "    for epoch in range(1, num_epochs + 1):\r\n",
        "        since = time.time()                                     # 학습 시간 계산\r\n",
        "        train(model, train_loader, optimizer)                   # train 데이터로 학습\r\n",
        "        train_loss, train_acc = evaluate(model, train_loader)   # train_loss, train_acc 계산\r\n",
        "        val_loss, val_acc = evaluate(model, val_loader)         # valid_loss, valid_acc 계산\r\n",
        "        \r\n",
        "        if val_acc>best_acc:  # update best accuracy\r\n",
        "            best_acc = val_acc\r\n",
        "            best_model_wts = copy.deepcopy(model.state_dict())\r\n",
        "        \r\n",
        "        time_elapsed = time.time() - since # 학습 시간 출력\r\n",
        "        print('-------------- epoch {} ----------------'.format(epoch))\r\n",
        "        print('train Loss: {:.4f}, Accuracy: {:.2f}%'.format(train_loss, train_acc))   \r\n",
        "        print('val Loss: {:.4f}, Accuracy: {:.2f}%'.format(val_loss, val_acc))\r\n",
        "        print('Completed in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60)) \r\n",
        "\r\n",
        "    model.load_state_dict(best_model_wts)  \r\n",
        "    return model\r\n",
        "\r\n",
        "model = train_model(mlp ,train_loader, test_loader, optimizer)  \t# 모델 학습시키기\r\n",
        "torch.save(model,'MLP.pt') "
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------- epoch 1 ----------------\n",
            "train Loss: 0.0086, Accuracy: 75.78%\n",
            "val Loss: 0.0080, Accuracy: 78.28%\n",
            "Completed in 6m 8s\n",
            "-------------- epoch 2 ----------------\n",
            "train Loss: 0.0076, Accuracy: 80.57%\n",
            "val Loss: 0.0069, Accuracy: 82.52%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 3 ----------------\n",
            "train Loss: 0.0063, Accuracy: 82.11%\n",
            "val Loss: 0.0061, Accuracy: 82.41%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 4 ----------------\n",
            "train Loss: 0.0038, Accuracy: 92.80%\n",
            "val Loss: 0.0047, Accuracy: 89.36%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 5 ----------------\n",
            "train Loss: 0.0063, Accuracy: 80.54%\n",
            "val Loss: 0.0058, Accuracy: 81.32%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 6 ----------------\n",
            "train Loss: 0.0030, Accuracy: 93.37%\n",
            "val Loss: 0.0043, Accuracy: 89.58%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 7 ----------------\n",
            "train Loss: 0.0036, Accuracy: 90.98%\n",
            "val Loss: 0.0072, Accuracy: 81.98%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 8 ----------------\n",
            "train Loss: 0.0012, Accuracy: 97.28%\n",
            "val Loss: 0.0011, Accuracy: 97.83%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 9 ----------------\n",
            "train Loss: 0.0008, Accuracy: 98.56%\n",
            "val Loss: 0.0008, Accuracy: 98.48%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 10 ----------------\n",
            "train Loss: 0.0010, Accuracy: 98.04%\n",
            "val Loss: 0.0013, Accuracy: 96.63%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 11 ----------------\n",
            "train Loss: 0.0007, Accuracy: 98.42%\n",
            "val Loss: 0.0009, Accuracy: 97.72%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 12 ----------------\n",
            "train Loss: 0.0010, Accuracy: 98.48%\n",
            "val Loss: 0.0030, Accuracy: 93.16%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 13 ----------------\n",
            "train Loss: 0.0008, Accuracy: 98.75%\n",
            "val Loss: 0.0039, Accuracy: 90.99%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 14 ----------------\n",
            "train Loss: 0.0005, Accuracy: 98.86%\n",
            "val Loss: 0.0007, Accuracy: 98.15%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 15 ----------------\n",
            "train Loss: 0.0001, Accuracy: 99.84%\n",
            "val Loss: 0.0012, Accuracy: 97.29%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 16 ----------------\n",
            "train Loss: 0.0002, Accuracy: 99.57%\n",
            "val Loss: 0.0005, Accuracy: 99.02%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 17 ----------------\n",
            "train Loss: 0.0005, Accuracy: 99.16%\n",
            "val Loss: 0.0008, Accuracy: 98.37%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 18 ----------------\n",
            "train Loss: 0.0002, Accuracy: 99.70%\n",
            "val Loss: 0.0004, Accuracy: 99.13%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 19 ----------------\n",
            "train Loss: 0.0001, Accuracy: 99.95%\n",
            "val Loss: 0.0005, Accuracy: 98.81%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 20 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0007, Accuracy: 98.48%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 21 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0003, Accuracy: 99.35%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 22 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0004, Accuracy: 99.24%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 23 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0006, Accuracy: 98.91%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 24 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0005, Accuracy: 99.13%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 25 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0005, Accuracy: 99.02%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 26 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0005, Accuracy: 99.13%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 27 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0005, Accuracy: 99.13%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 28 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0004, Accuracy: 99.24%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 29 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0004, Accuracy: 99.24%\n",
            "Completed in 0m 34s\n",
            "-------------- epoch 30 ----------------\n",
            "train Loss: 0.0000, Accuracy: 100.00%\n",
            "val Loss: 0.0004, Accuracy: 99.13%\n",
            "Completed in 0m 34s\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmk9kEN2VpbV",
        "outputId": "3cdf32d6-9ada-4225-a197-3ecd5c7d1899"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test MLP"
      ],
      "metadata": {
        "id": "wDIf1rOTVtqw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "source": [
        "model=torch.load('MLP.pt')\r\n",
        "model.eval()\r\n",
        "loss, acc = evaluate(model, test_loader)\r\n",
        "\r\n",
        "print('Test Accuracy: {:.4f}'.format(acc))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 99.3485\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vck5U8sCcPgR",
        "outputId": "4328139b-6c13-4a80-9d78-783fa4d11c1d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "source": [
        "from sklearn.metrics import classification_report\r\n",
        "\r\n",
        "def prediction(model, data_loader):\r\n",
        "    model.eval()\r\n",
        "    predlist=torch.zeros(0,dtype=torch.long, device='cpu')\r\n",
        "    lbllist=torch.zeros(0,dtype=torch.long, device='cpu')\r\n",
        "    \r\n",
        "    with torch.no_grad():\r\n",
        "      for i, (data, label) in enumerate(data_loader):\r\n",
        "        data = data.to(DEVICE)        # 데이터 DEVICE에 할당\r\n",
        "        label = label.to(DEVICE)      # 라벨 값 DEVICE에 할당\r\n",
        "        outputs = model(data)         # 예측\r\n",
        "        _, preds = torch.max(outputs, 1)  # 가장 높은 확률 가지는 class 예측\r\n",
        "\r\n",
        "        # Batch 단위 예측값 append 하기\r\n",
        "        predlist=torch.cat([predlist,preds.view(-1).cpu()])\r\n",
        "        lbllist=torch.cat([lbllist,label.view(-1).cpu()])\r\n",
        "        \r\n",
        "    # Classification Report\r\n",
        "    print(classification_report(lbllist.numpy(), predlist.numpy())) # 클래스별 accuracy, recall, f1-score \r\n",
        "    return "
      ],
      "outputs": [],
      "metadata": {
        "id": "ox29vVfeW4gv"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "source": [
        "prediction(model, test_loader)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:477: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99       503\n",
            "           1       0.99      1.00      0.99       418\n",
            "\n",
            "    accuracy                           0.99       921\n",
            "   macro avg       0.99      0.99      0.99       921\n",
            "weighted avg       0.99      0.99      0.99       921\n",
            "\n"
          ]
        }
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Qzh5k18iL0E",
        "outputId": "3a636197-92b5-412f-d145-0a073b8c6df1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [],
      "outputs": [],
      "metadata": {
        "id": "X4VhHCl6k6IQ"
      }
    }
  ]
}